所有CK组件都可以在和！ 使用具有通用JSON API的集体知识工作流框架来统一AI以进行协作实验和优化 请注意，Caffe2已移至 GitHub源代码树，因此此处的某些软件包可能无法正常工作。 介绍 在将大部分的“研究”时间都花在了AI创新上之后，而不是在处理众多且不断变化的AI引擎，它们的API以及整个软件和硬件堆栈之后，我们决定采用另一种方法。 我们开始添加现有的AI框架，包括 ， ， ， ， ， CNTK和MVNC 。 非侵入式开源集体知识工作流框架（CK） 。 CK允许使用JSON API将各种版本的AI框架以及库，编译器，工具，模型和数据集作为统一的和可重用的组件插入，从而在Linux，Windows，MacOS和Android上自动化和自定义其安装（而不是使用ad- hoc脚本），并提供简单的JSON API进行常见操作（例如预测和培训）（请参阅demo ）。

GameWeb是用于实验游戏研究的基于Web的平台。 GameWeb允许用户从Web界面跟踪鼠标移动，创建，调整，暂停和监视游戏。 基于Apache，MySQL和PHP项目。

我们提出并通过实验演示了基于模式分割复用的无源光网络。 两种单独的线性偏振（LP）模式已成功通过1.8公里的低模串扰少模光纤传输，并以无错误的性能进行了多路分解。

A high-power fiber laser in an all-fiber format is reported. The system consists of 36 pump ports, which use both counter and forward pump configuration. In the experiment, 1 008-W output power is obtained when 24 pump ports are used with a total pump power of 1 477 W. The optical-to-optical conversion efficiency is 68% and the 3-dB bandwidth of laser output increases with output power. Presently, the output power is only limited by the pump source. It can be predicted that the laser power can b

数据中心的动态能量模型实验和数值模拟

We present a full three-dimensional, featured-data algorithm for time-domain fluorescence diffuse optical tomography that inverts the Laplace-transformed time-domain coupled diffusion equations and employs a pair of appropriate transform-factors to effectively separate the fluorescent yield and life

armacmp：Arm使用Armadillo将线性代数R代码自动编译为C ++

Gradle_Experimental_NDK 基于LLDB调试C/C++ 的Demo

Noise and Vibration Analysis is a complete and practical guide that combines both signal processing and modal analysis theory with their practical application in noise and vibration analysis. It provides an invaluable, integrated guide for practicing engineers as well as a suitable introduction for students new to the topic of noise and vibration. Taking a practical learning approach, Brandt includes exercises that allow the content to be developed in an academic course framework or as supplementary material for private and further study.

最大流/最小割算法的简介，理解常用最大流最小割概念的文献，值得学习。 minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style “push-relabel” methods and algorithms based on Ford- Fulkerson style “augmenting paths.” We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.